Exploratory Data Analysis

Jordan Farrer
2016-01-19

Make Data Your BFF

Agenda

Role of EDA
tidyverse Fundamentals
EDA Examples

Role of EDA

Data Analysis Lifecycle

Source: r4ds.had.co.nz/

What is EDA?

Exporatory Data Analysis : ask questions, uncover features of your data, but no conclusive results

Objective: know your data inside and out
There is no right way
It's a creative process
Better questions → better understanding

Balancing Act

Data Integrity vs Data Understanding

balance

Role	More likely to …
Engineer	Focus on data integrity
Statistician	Jump to complex, intricate questions
Data Scientist	Balance both

Roadmap of Questions

Data Integrity
- Record counts
- Missing values
- Unusual values
Data Understanding
- Variation
- Covariation
  - Two Continuous Variables
  - One Continuous and One Categorical
  - Two Categorical
- Patterns / Anomolies

tidyverse Fundamentals

tidyverse

library(tidyverse)

tidyverse is a collection of R packages that share common philosophies and are designed to work together

created mostly by Hadley Wickam and maintained by RStudio

R-God

Packages Discussed Today

dplyr : data manipulation

ggplot2 : grammar of graphics

dplyr

Set of simple “verbs” - functions that correspond to the most common data manipulation tasks

help you translate those thoughts into code
purposefully constrains your options
creates readable code (no guessing why you're doing that)

The Verbs

dplyr verb	SQL equivalent	Action
`filter()`	WHERE	Limit based on condition
`select()`	SELECT	Choose only certain variables
`distinct()`	DISTINCT	De-duplicate result-set
`arrange()`	ORDER BY	Order results
`rename()`	SELECT	Rename variables
`mutate()`	SELECT	Create new variable
`group_by()`	GROUP BY	Group rows
`summarise()`	SELECT	Create new variable in grouped setting

Flights Data

library(nycflights13)

flights

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>
1   2013     1     1      517            515         2      830
2   2013     1     1      533            529         4      850
3   2013     1     1      542            540         2      923
4   2013     1     1      544            545        -1     1004
5   2013     1     1      554            600        -6      812
6   2013     1     1      554            558        -4      740
7   2013     1     1      555            600        -5      913
8   2013     1     1      557            600        -3      709
9   2013     1     1      557            600        -3      838
10  2013     1     1      558            600        -2      753
# ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

Dataset

336,776 flights that departed from New York City in 2013
From the US Bureau of Transportation Statistics

filter - 1

flights %>%
  filter(month == 1 &  day == 1)

# A tibble: 842 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>
1   2013     1     1      517            515         2      830
2   2013     1     1      533            529         4      850
3   2013     1     1      542            540         2      923
4   2013     1     1      544            545        -1     1004
5   2013     1     1      554            600        -6      812
6   2013     1     1      554            558        -4      740
7   2013     1     1      555            600        -5      913
8   2013     1     1      557            600        -3      709
9   2013     1     1      557            600        -3      838
10  2013     1     1      558            600        -2      753
# ... with 832 more rows, and 12 more variables: sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

filter - 2

flights %>%
  filter(dest %in% c("PHL","SLC") & month <= 6)

# A tibble: 2,116 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>
1   2013     1     1      655            655         0     1021
2   2013     1     1      908            915        -7     1004
3   2013     1     1     1047           1050        -3     1401
4   2013     1     1     1245           1245         0     1616
5   2013     1     1     1323           1300        23     1651
6   2013     1     1     1543           1550        -7     1933
7   2013     1     1     1600           1610       -10     1712
8   2013     1     1     1909           1912        -3     2239
9   2013     1     1     1915           1920        -5     2238
10  2013     1     1     2000           2000         0     2054
# ... with 2,106 more rows, and 12 more variables: sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

select

flights %>%
  filter(dest %in% c("PHL","SLC") & month <= 6) %>%
  select(origin, dest, carrier)

# A tibble: 2,116 × 3
   origin  dest carrier
    <chr> <chr>   <chr>
1     JFK   SLC      DL
2     LGA   PHL      US
3     JFK   SLC      DL
4     JFK   SLC      DL
5     EWR   SLC      DL
6     JFK   SLC      DL
7     JFK   PHL      9E
8     JFK   SLC      B6
9     JFK   SLC      DL
10    JFK   PHL      9E
# ... with 2,106 more rows

distinct

flights %>%
  filter(dest %in% c("PHL","SLC") & month <= 6) %>%
  select(origin, dest, carrier) %>% 
  distinct()

# A tibble: 8 × 3
  origin  dest carrier
   <chr> <chr>   <chr>
1    JFK   SLC      DL
2    LGA   PHL      US
3    EWR   SLC      DL
4    JFK   PHL      9E
5    JFK   SLC      B6
6    EWR   PHL      EV
7    JFK   PHL      US
8    JFK   PHL      DL

arrange

flights %>%
  filter(dest %in% c("PHL","SLC") & month <= 6) %>%
  select(origin, dest, carrier) %>% 
  distinct() %>%
  arrange(origin, desc(dest), carrier)

# A tibble: 8 × 3
  origin  dest carrier
   <chr> <chr>   <chr>
1    EWR   SLC      DL
2    EWR   PHL      EV
3    JFK   SLC      B6
4    JFK   SLC      DL
5    JFK   PHL      9E
6    JFK   PHL      DL
7    JFK   PHL      US
8    LGA   PHL      US

rename

flights %>%
  filter(dest %in% c("PHL","SLC") & month <= 6) %>%
  select(origin, dest, carrier) %>% 
  distinct() %>%
  arrange(origin, desc(dest), carrier) %>%
  rename(airline = carrier)

# A tibble: 8 × 3
  origin  dest airline
   <chr> <chr>   <chr>
1    EWR   SLC      DL
2    EWR   PHL      EV
3    JFK   SLC      B6
4    JFK   SLC      DL
5    JFK   PHL      9E
6    JFK   PHL      DL
7    JFK   PHL      US
8    LGA   PHL      US

mutate

flights %>%
  mutate(
    gain = arr_delay - dep_delay
  , speed = distance / air_time * 60
  , gain_per_hour = gain / (air_time / 60)
  ) %>% 
  select(dep_delay, arr_delay, gain, distance, distance, air_time, speed, gain_per_hour)

# A tibble: 336,776 × 7
   dep_delay arr_delay  gain distance air_time    speed gain_per_hour
       <dbl>     <dbl> <dbl>    <dbl>    <dbl>    <dbl>         <dbl>
1          2        11     9     1400      227 370.0441      2.378855
2          4        20    16     1416      227 374.2731      4.229075
3          2        33    31     1089      160 408.3750     11.625000
4         -1       -18   -17     1576      183 516.7213     -5.573770
5         -6       -25   -19      762      116 394.1379     -9.827586
6         -4        12    16      719      150 287.6000      6.400000
7         -5        19    24     1065      158 404.4304      9.113924
8         -3       -14   -11      229       53 259.2453    -12.452830
9         -3        -8    -5      944      140 404.5714     -2.142857
10        -2         8    10      733      138 318.6957      4.347826
# ... with 336,766 more rows

group_by / summarise - 1

flights %>%
  group_by(origin) %>%
  summarise(
    n = n()
    , avg_delay = mean(dep_delay, na.rm = TRUE)
  )

# A tibble: 3 × 3
  origin      n avg_delay
   <chr>  <int>     <dbl>
1    EWR 120835  15.10795
2    JFK 111279  12.11216
3    LGA 104662  10.34688

summarise

flights %>%
  summarise(
    n = n()
    , destinations = n_distinct(dest)
    , avg_delay = mean(dep_delay, na.rm = TRUE)
  )

# A tibble: 1 × 3
       n destinations avg_delay
   <int>        <int>     <dbl>
1 336776          105  12.63907

group_by / summarise - 2

flights %>%
  group_by(origin) %>%
  summarise(
      destinations = n_distinct(dest)
    , avg_distance = mean(distance, na.rm = TRUE)
  )

# A tibble: 3 × 3
  origin destinations avg_distance
   <chr>        <int>        <dbl>
1    EWR           86    1056.7428
2    JFK           70    1266.2491
3    LGA           68     779.8357

group_by / summarise - 3

What is the farthest flight to leave NYC?

flights %>%
  group_by(origin, dest) %>%
  summarise(
      n = n()
    , min = min(distance)
    , max = max(distance)
    , median = median(distance)
  ) %>%
  arrange(desc(median))

Source: local data frame [224 x 6]
Groups: origin [3]

   origin  dest     n   min   max median
    <chr> <chr> <int> <dbl> <dbl>  <dbl>
1     JFK   HNL   342  4983  4983   4983
2     EWR   HNL   365  4963  4963   4963
3     EWR   ANC     8  3370  3370   3370
4     JFK   SFO  8204  2586  2586   2586
5     JFK   OAK   312  2576  2576   2576
6     JFK   SJC   329  2569  2569   2569
7     EWR   SFO  5127  2565  2565   2565
8     JFK   SMF   284  2521  2521   2521
9     JFK   LAX 11262  2475  2475   2475
10    JFK   BUR   371  2465  2465   2465
# ... with 214 more rows

ggplot2

Grammar of Graphics : abstraction of how to build visual stories with data
Forget powerpoint or excel

ggplot2 Examples - 1

ggplot2 Examples - 2

ggplot2 Examples - 3

ggplot2 Examples - 4

ggplot2 Examples - 5

Basic Structure

ggplot(data, aes(x = *, y = *)) + geom_***

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg)) +
  geom_point()

plot of chunk unnamed-chunk-14

Fundemental Concept

Geometric objects (geom_*) have aesthetic attributes (aes(x = *, color = *))

When we visualize data we MAP our data to aesethics.

Common Geoms

geom_point
geom_bar
geom_boxplot
geom_histogram
geom_line
geom_smooth

See documentation (docs.ggplot2.org/current) for more

Common Aesthetics

x
y
colour
fill
alpha
size
shape

geom_bar

ggplot(diamonds, aes(x = cut)) +
  geom_bar()

plot of chunk unnamed-chunk-15

geom_point with colour

ggplot(diamonds, aes(x = carat, y = price, colour = cut)) +
  geom_point() +
  labs(title = "Diamonds Dataset", subtitle = "Carat vs Price")

plot of chunk unnamed-chunk-16

Example EDA

Data Integrity Checks
Intricate Questions

Look at the Data

flights

# A tibble: 336,776 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>
1   2013     1     1      517            515         2      830
2   2013     1     1      533            529         4      850
3   2013     1     1      542            540         2      923
4   2013     1     1      544            545        -1     1004
5   2013     1     1      554            600        -6      812
6   2013     1     1      554            558        -4      740
7   2013     1     1      555            600        -5      913
8   2013     1     1      557            600        -3      709
9   2013     1     1      557            600        -3      838
10  2013     1     1      558            600        -2      753
# ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

Data has 336776 rows and 19 columns using:

`r nrow(flights)` and `r ncol(flights)`

Missing Values

flights %>%
  filter(!complete.cases(.))

# A tibble: 9,430 × 19
    year month   day dep_time sched_dep_time dep_delay arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>
1   2013     1     1     1525           1530        -5     1934
2   2013     1     1     1528           1459        29     2002
3   2013     1     1     1740           1745        -5     2158
4   2013     1     1     1807           1738        29     2251
5   2013     1     1     1939           1840        59       29
6   2013     1     1     1952           1930        22     2358
7   2013     1     1     2016           1930        46       NA
8   2013     1     1       NA           1630        NA       NA
9   2013     1     1       NA           1935        NA       NA
10  2013     1     1       NA           1500        NA       NA
# ... with 9,420 more rows, and 12 more variables: sched_arr_time <int>,
#   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#   minute <dbl>, time_hour <dttm>

sapply(flights, function(x) sum(is.na(x)))

          year          month            day       dep_time sched_dep_time 
             0              0              0           8255              0 
     dep_delay       arr_time sched_arr_time      arr_delay        carrier 
          8255           8713              0           9430              0 
        flight        tailnum         origin           dest       air_time 
             0           2512              0              0           9430 
      distance           hour         minute      time_hour 
             0              0              0              0

Summary of Columns

flights %>%
  select(1:10) %>%
  summary

      year          month             day           dep_time   
 Min.   :2013   Min.   : 1.000   Min.   : 1.00   Min.   :   1  
 1st Qu.:2013   1st Qu.: 4.000   1st Qu.: 8.00   1st Qu.: 907  
 Median :2013   Median : 7.000   Median :16.00   Median :1401  
 Mean   :2013   Mean   : 6.549   Mean   :15.71   Mean   :1349  
 3rd Qu.:2013   3rd Qu.:10.000   3rd Qu.:23.00   3rd Qu.:1744  
 Max.   :2013   Max.   :12.000   Max.   :31.00   Max.   :2400  
                                                 NA's   :8255  
 sched_dep_time   dep_delay          arr_time    sched_arr_time
 Min.   : 106   Min.   : -43.00   Min.   :   1   Min.   :   1  
 1st Qu.: 906   1st Qu.:  -5.00   1st Qu.:1104   1st Qu.:1124  
 Median :1359   Median :  -2.00   Median :1535   Median :1556  
 Mean   :1344   Mean   :  12.64   Mean   :1502   Mean   :1536  
 3rd Qu.:1729   3rd Qu.:  11.00   3rd Qu.:1940   3rd Qu.:1945  
 Max.   :2359   Max.   :1301.00   Max.   :2400   Max.   :2359  
                NA's   :8255      NA's   :8713                 
   arr_delay          carrier         
 Min.   : -86.000   Length:336776     
 1st Qu.: -17.000   Class :character  
 Median :  -5.000   Mode  :character  
 Mean   :   6.895                     
 3rd Qu.:  14.000                     
 Max.   :1272.000                     
 NA's   :9430

Unusual Values

ggplot(flights, aes(x = air_time)) +
  geom_histogram()

plot of chunk unnamed-chunk-21

Gapminder Dataset

Gapminder is a non-profit in Stockholm, Sweden that puts out data on development metrics

library(gapminder)
gapminder

# A tibble: 1,704 × 6
       country continent  year lifeExp      pop gdpPercap
        <fctr>    <fctr> <int>   <dbl>    <int>     <dbl>
1  Afghanistan      Asia  1952  28.801  8425333  779.4453
2  Afghanistan      Asia  1957  30.332  9240934  820.8530
3  Afghanistan      Asia  1962  31.997 10267083  853.1007
4  Afghanistan      Asia  1967  34.020 11537966  836.1971
5  Afghanistan      Asia  1972  36.088 13079460  739.9811
6  Afghanistan      Asia  1977  38.438 14880372  786.1134
7  Afghanistan      Asia  1982  39.854 12881816  978.0114
8  Afghanistan      Asia  1987  40.822 13867957  852.3959
9  Afghanistan      Asia  1992  41.674 16317921  649.3414
10 Afghanistan      Asia  1997  41.763 22227415  635.3414
# ... with 1,694 more rows

Data Integrity - 1

Check for missing data

gapminder %>%
  filter(!complete.cases(.))

# A tibble: 0 × 6
# ... with 6 variables: country <fctr>, continent <fctr>, year <int>,
#   lifeExp <dbl>, pop <int>, gdpPercap <dbl>

Data Integrity - 2

Do all the countries have the same number of years?

gapminder %>% 
  group_by(country) %>% 
  summarise(years_per_country = n()) %>%
  group_by(years_per_country) %>%
  summarise(countries = n())

# A tibble: 1 × 2
  years_per_country countries
              <int>     <int>
1                12       142

Data Integrity - 3

What years are included in the dataset?

gapminder %>%
  distinct(year)

# A tibble: 12 × 1
    year
   <int>
1   1952
2   1957
3   1962
4   1967
5   1972
6   1977
7   1982
8   1987
9   1992
10  1997
11  2002
12  2007

Data Integrity - 4

  gapminder %>%
    group_by(continent) %>%
    summarize(n_obs = n(), n_countries = n_distinct(country)) %>%
    mutate(years = n_obs / n_countries)

# A tibble: 5 × 4
  continent n_obs n_countries years
     <fctr> <int>       <int> <dbl>
1    Africa   624          52    12
2  Americas   300          25    12
3      Asia   396          33    12
4    Europe   360          30    12
5   Oceania    24           2    12

Data Integrity - 5

What is the median life expectancy for each continent in 2007 (last year)?

gapminder %>%
  filter(year == 2007) %>%
  group_by(continent) %>%
  summarise(lifeExp = median(lifeExp))

# A tibble: 5 × 2
  continent lifeExp
     <fctr>   <dbl>
1    Africa 52.9265
2  Americas 72.8990
3      Asia 72.3960
4    Europe 78.6085
5   Oceania 80.7195

Data Integrity - 6

Basic summary

gapminder %>%
  summary()

        country        continent        year         lifeExp     
 Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
 Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
 Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
 Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
 Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
 Australia  :  12                  Max.   :2007   Max.   :82.60  
 (Other)    :1632                                                
      pop              gdpPercap       
 Min.   :6.001e+04   Min.   :   241.2  
 1st Qu.:2.794e+06   1st Qu.:  1202.1  
 Median :7.024e+06   Median :  3531.8  
 Mean   :2.960e+07   Mean   :  7215.3  
 3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
 Max.   :1.319e+09   Max.   :113523.1

Plots by Data Types

Data	Plots
One Continuous	Histogram
One Continuous + One Categorical	Boxplot
Two Continuous	Scatter Plot
Three Continuous	Scatter Plot + Size
Two Continuous + One Categorical	Scatter Plot + Color
Categorical with reasonable number of levels	Faceting!!

Note: Time is always the x-axis.

One Continuous

What is the distribution of life expectancy in 2007?

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = lifeExp)) +
  geom_histogram(binwidth = 2)

plot of chunk unnamed-chunk-29

One Continuous + One Categorical

What is the distribution of life expectancy in 2007 by continent?

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = continent, y = lifeExp)) +
  geom_boxplot()

plot of chunk unnamed-chunk-30

Who are the Outliers?

gapminder %>%
  filter(year == 2007 & continent %in% c("Americas","Asia")) %>%
  group_by(continent) %>%
  filter(rank(lifeExp) == 1)

Source: local data frame [2 x 6]
Groups: continent [2]

      country continent  year lifeExp      pop gdpPercap
       <fctr>    <fctr> <int>   <dbl>    <int>     <dbl>
1 Afghanistan      Asia  2007  43.828 31889923  974.5803
2       Haiti  Americas  2007  60.916  8502814 1201.6372

Two Continuous

What is the relationship between GDP per capita and life expectancy?

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) + 
  geom_point()

plot of chunk unnamed-chunk-32

Let's log that!!

Relationship is clearer

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) + 
  geom_point() +
  scale_x_log10()

plot of chunk unnamed-chunk-33

Two Continuous + One Categorical (few)

What is the relationship between GDP per capita and life expectancy by continent?

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) + 
  geom_point() +
  scale_x_log10()

Two Continuous + One Categorial (many)

What is the relationship between GDP per capita and life expectancy over time?

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = year)) + 
  geom_point() +
  scale_x_log10()

plot of chunk unnamed-chunk-35

Three Continuous

What is the relationship between GDP per capita, life expectancy, and population in 2007?

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, size = pop)) + 
  geom_point() +
  scale_x_log10()

plot of chunk unnamed-chunk-36

One Continuous + One Categorical (Time)

What does the distribution of life expectancy look like by continent?

gapminder %>%
  ggplot(aes(x = year, y = lifeExp, colour = continent)) +
  geom_point() # position = 'jitter'

plot of chunk unnamed-chunk-37

Let's Facet!

One Continuous + Two Categorical

What does the distribution of life expectancy look like by continent?

gapminder %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_point() +
  geom_smooth(method = 'loess', se = FALSE) + 
  facet_wrap(~ continent)

plot of chunk unnamed-chunk-38

Data Integrity (again)

Who is Oceania?

gapminder %>%
  filter(continent == "Oceania") %>%
  distinct(country)

# A tibble: 2 × 1
      country
       <fctr>
1   Australia
2 New Zealand

Data Integrity (again)

What if we included them as Asia?

gapminder %>%
  mutate(continent = forcats::fct_recode(continent, Asia = "Oceania")) %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_point() +
  geom_smooth(method = 'loess', se = FALSE) + 
  facet_wrap(~ continent)

plot of chunk unnamed-chunk-40

One Continuous + Two Categorical

What if we wanted line charts instead?

gapminder %>%
  ggplot(aes(x = year, y = lifeExp)) +
  geom_point(alpha = 2/3) +
  geom_line(aes(group = country), alpha = 1/3) +
  geom_smooth(method = 'loess', se = FALSE) + 
  facet_wrap(~ continent)

plot of chunk unnamed-chunk-41

One Continuous + One Categorical

What is the distribution of life expectancy (in 2007) by continent? (non-boxplot

gapminder %>%
  filter(year == 2007) %>%
  ggplot(aes(x = lifeExp)) +
  geom_histogram(binwidth = 2) +
  facet_wrap(~ continent)

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Two Continuous + Two Categorical

What is the relationship between GDP per capita and life expectancy across continents and time?

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent, alpha = year)) + 
  geom_point() + 
  scale_x_log10()

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Add in Regression Line

gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) + 
  geom_point(aes(alpha = year)) + 
  geom_smooth(method = 'lm', se = FALSE) + 
  scale_x_log10()

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Linear Model for Each Year and Continent

gapminder %>%
  filter(country != "Kuwait") %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) + 
  geom_point() + 
  geom_smooth(method = 'lm', se = FALSE) + 
  facet_wrap(~ year) + 
  scale_x_log10()

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Particular Observations

Are there any trends in particular countries over time?

gapminder %>%
  filter(country %in% c("Canada", "Rwanda", "Cambodia", "Mexico", "Zimbabwe")) %>%
  ggplot(aes(x = year, y = lifeExp, colour = country)) + 
  geom_point() + 
  geom_line() +
  labs(title = "Sad Evidence of Human Conflict")

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Let's end on a positive note

How is the distribution of life expectancy evolving over time?

gapminder %>%
  ggplot(aes(x = factor(year), y = lifeExp)) +
  geom_boxplot() +
  labs(x = "year")

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References

R for Data Science

dplyr tutorial

gapminder ggplot tutorial

ggplot to learn visualization